R for the Rest of Us: A Statistics-Free Introduction

Learn how to use R for everything from workload automation and creating online reports, to interpreting data, map making, and more. Written by the founder of a very popular online training platform for the R programming language! The R programming language is a remarkably powerful tool for data analysis and visualization, but its steep learning curve can be intimidating for some. If you just want to automate repetitive tasks or visualize your data, without the need for complex math, R for the Rest of Us is for you. Inside you’ll find a crash course in R, a quick tour of the RStudio programming environment, and a collection of real-word applications that you can put to use right away. You’ll learn how to create informative visualizations, streamline report generation, and develop interactive websites—whether you’re a seasoned R user or have never written a line of R code. You’ll also learn how to: • Manipulate, clean, and parse your data with tidyverse packages like dplyr and tidyr to make data science operations more user-friendly • Create stunning and customized plots, graphs, and charts with ggplot2 to effectively communicate your data insights • Import geospatial data and write code to produce visually appealing maps automatically • Generate dynamic reports, presentations, and interactive websites with R Markdown and Quarto that seamlessly integrate code, text, and graphics • Develop custom functions and packages tailored to your specific needs, allowing you to extend R’s functionality and automate complex tasks Unlock a treasure trove of techniques to transform the way you work. With R for the Rest of Us, you’ll discover the power of R to get stuff done. No advanced statistics degree required. Cover Praise for R for the Rest of Us Title Page Copyright Dedication About the Author and Technical Reviewer Acknowledgments Introduction Isn’t R Just for Statistical Analysis? Who This Book Is For About This Book Part I: Visualizations 1. An R Programming Crash Course Setting Up Installing R and RStudio Exploring the RStudio Interface R Script Files Basic R Syntax Arithmetic Operators Comparison Operators Functions Working with Data Importing Data Saving Data as Objects Installing Packages RStudio Projects Data Analysis with the tidyverse tidyverse Functions The tidyverse Pipe Comments How to Get Help Summary Additional Resources 2. Principles of Data Visualization The Drought Visualization The Grammar of Graphics Working with ggplot Mapping Data to Aesthetic Properties Choosing the Geometric Objects Altering Aesthetic Properties Setting a Theme Re-creating the Drought Visualization Plotting One Region and Year Changing Aesthetic Properties Faceting the Plot Adding Final Polishes The Complete Visualization Code Summary Additional Resources 3. Custom Data Visualization Themes Styling a Plot with a Custom Theme An Example Plot The BBC’s Custom Theme The BBC Theme Components Function Definition Text Legend Axes Grid Lines Background Small Multiples Color Summary Additional Resources 4. Maps and Geospatial Data A Brief Primer on Geospatial Data The Geometry Type The Dimensions The Bounding Box The Coordinate Reference System The geometry Column Re-creating the COVID-19 Map Importing the Data Calculating Daily COVID-19 Cases Calculating Incidence Rates Adding Geospatial Data Making the Map Making Your Own Maps Importing Raw Data Accessing Geospatial Data with R Functions Using Appropriate Projections Wrangling Geospatial Data Summary Additional Resources 5. Designing Effective Tables Creating a Data Frame Table Design Principles Minimize Clutter Differentiate the Header from the Body Align Appropriately Use the Correct Level of Precision Use Color Intentionally Add a Data Visualization Where Appropriate Summary Additional Resources Part II: Reports, Presentations, and Websites 6. R Markdown Reports Creating an R Markdown Document Document Structure The YAML Metadata The R Code Chunks Markdown Text Inline R Code Running Code Chunks Interactively Quarto Summary Additional Resources 7. Parameterized Reporting Report Templates in R Markdown Defining Parameters Generating Numbers with Parameters Including Parameters in Visualization Code Creating an R Script Knitting the Document with Code Creating a Tibble with Parameter Data Best Practices Summary Additional Resources 8. Slideshow Presentations Why Use xaringan? How xaringan Works Creating a New Slide Adjusting the Size of Figures Revealing Content Incrementally Aligning Content with Content Classes Adding Background Images to Slides Applying CSS to Slides Custom CSS Themes The xaringanthemer Package Summary Additional Resources 9. Websites Creating a New distill Project The Project Files R Markdown Documents The _site.yml File Building the Site Applying Custom CSS Working with Website Content Applying distill Layouts Making the Content Interactive Hosting the Website Cloud Hosting GitHub Hosting Summary Additional Resources 10. Quarto Creating a Quarto Document Comparing R Markdown and Quarto The format and execute YAML Fields Individual Code Chunk Options Dashes in Option Names The Render Button Parameterized Reporting Making Presentations Revealing Content Incrementally Aligning Content and Adding Background Images Customizing Your Slides with Themes and CSS Making Websites Building the Website Setting Options Changing the Website’s Appearance Adjusting the Title and Navigation Bar Creating Wider Layouts Hosting Your Website on GitHub Pages and Quarto Pub Summary Additional Resources Part III: Automation and Collaboration 11. Automatically Accessing Online Data Importing Data from Google Sheets with googlesheets4 Connecting to Google Reading Data from a Sheet Using the Data in R Markdown Importing Only Certain Columns Accessing Census Data with tidycensus Connecting to the Census Bureau with an API Key Working with Decennial Census Data Identifying Census Variable Values Using Multiple Census Variables Analyzing Census Data Using a Summary Variable Visualizing American Community Survey Data Making Charts Making Population Maps with the geometry Argument Summary Additional Resources 12. Creating Functions and Packages Creating Your Own Functions Writing a Simple Function Adding Arguments Creating a Function to Format Race and Ethnicity Data Using ... to Pass Arguments to Another Function Creating a Package Starting the Package Adding Functions with use_r() Checking the Package with devtools Adding Dependency Packages Referring to Functions Correctly Creating Documentation with Roxygen Adding a License and Metadata Writing Additional Functions Installing the Package Summary Additional Resources Wrapping Up Index Learn how to use R for everything from workload automation and creating online reports, to interpreting data, map making, and more.Written by the founder of a very popular online training platform for the R programming language! For statisticians, R is the go-to programming language for complex numerical analysisbut it comes in handy for a lot more than that. In R Without Statistics youll discover ways that R can be used by the rest of us! Packed with real-world examples and easy-to-follow coding instructions, it introduces Rs application in a wide range of non-statistical tasks, from data visualization and interpreting survey results, to map plotting and automating workloads.Each chapter features an actual R programmer who achieved something novel using the language, and then covers the case study and code samples demonstrating exactly how they did it. Whether its creating visualizations for Scientific American, applying a consistent theme to BBC graphics, organizing professional government reports, or effectively mapping the spread of COVID-19, R offers a unique way to transform your work.